Jiachen Xu: h-index, Total Citations, and Citation Map
Jiachen Xu's h-index is 6 (4 i10-index, 102+ total citations across 22+ publications) according to Google Scholar as of May 2026. Jiachen Xu is affiliated with PhD Candidate, Carnegie Mellon University.
Jiachen Xu is a researcher affiliated with PhD Candidate, Carnegie Mellon University, specializing in Machine Learning, Neural Network Accelerator, Computer Architecture. Their work has been cited 102 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jiachen Xu's Citation Metrics
Bibliometric impact based on 22 indexed publications.
- H-Index
- 6
- i10-Index
- 4
- Total Citations
- 102
- Citing Countries
- 25
As of May 2026.
Jiachen Xu has an h-index of 6 and 102 total citations across 22 publications, with research cited by institutions in 25 countries.
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Global Impact Map
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Top Cited Works
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Bayesian neural networks for identification and classification of radio frequency transmitters using power amplifiers’ nonlinearity signatures
202125
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
39 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered machine-learning-based RF transmitter authentication by leveraging power amplifier nonlinearity signatures, establishing a framework for hardware-level security in wireless communications.
The researcher developed a Bayesian neural network framework for identifying radio frequency transmitters by leveraging power amplifier nonlinearity signatures, establishing a novel approach to signal classification.
The researcher developed a real-time, multichannel neural spike sorting framework using convolutional neural networks to handle many-class classification in electrophysiology.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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